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Ground-Penetrating Radar (GPR) has recently become a powerful geophysical technique to characterize soil moisture at the field scale. We developed a data assimilation scheme to simultaneously estimate the vertical soil moisture profile and hydraulic parameters from time-lapse GPR measurements. The assimilation scheme includes a soil hydrodynamic model to simulate the soil moisture dynamics, a full-wave electromagnetic wave propagation model, and petrophysical relationship to link the state variable with the GPR data and a maximum likelihood ensemble assimilation algorithm. The hydraulic parameters are estimated jointly with the soil moisture using a state augmentation technique. The approach allows for the direct assimilation of GPR data, thus maximizing the use of the information. The proposed approach was validated by numerical experiments assuming wrong initial conditions and hydraulic parameters. The synthetic soil moisture profiles were generated by the Hydrus-1D model, which then were used by the electromagnetic model and petrophysical relationship to create 'observed' GPR data. The results show that the data assimilation significantly improves the accuracy of the hydrodynamic model prediction. Compared with the surface soil moisture assimilation, the GPR data assimilation better estimates the soil moisture profile and hydraulic parameters. The results also show that the estimated soil moisture profile in the loamy sand and silt soils converge to the 'true' state more rapidly than in the clay one. Of the three unknown parameters of the Mualem-van Genuchten model, the estimation of n is more accurate than that of α and Ks. The approach shows a great promise to use GPR measurements for the soil moisture profile and hydraulic parameter estimation at the field scale. [ABSTRACT FROM AUTHOR]

The ensemble Kalman smoother (EnKS) is employed to estimate surface and subsurface soil moisture and surface energy fluxes during the Southern Great Plains 1997 (SGP97) experiment through the assimilation of observed L band radiobrightness temperatures. Previous work using the ensemble Kalman filter (EnKF) and a simple smoother demonstrated that soil moisture estimation is a reanalysis-type problem. The EnKF uses observations as they become available to update the current state. The EnKS takes the EnKF estimate as its first guess. However, in addition to updating the current state it also updates the best estimate at previous times. The performance of the EnKS is compared to the EnKF and the ensemble open loop in which no measurements are assimilated. Estimated surface soil moisture is compared to gravimetric observations at three locations. Root zone (5-100 cm) soil moisture is evaluated by comparing the resultant latent heat flux to flux tower observations. In a fixed lag smoother, observations are used to update past estimates within a fixed time window. The EnKS can be implemented in a fixed lag formulation in problems with limited memory such as soil moisture estimation. It is shown that there is a trade-off to be made between the improved accuracy with longer lag and the increased computational cost incurred. It is demonstrated that the EnKS is a relatively inexpensive state estimation algorithm suited to operational data assimilation. [ABSTRACT FROM AUTHOR]

Future pathfinder missions such as NASA's Hydrosphere State (Hydros) and ESA's Soil Moisture and Ocean Salinity (SMOS) will provide satellite-based global observations of surface (0-5 cm) soil moisture. In previous work an ensemble Kalman filter was used to estimate soil moisture, related states, and fluxes by merging noisy low-frequency microwave observations with the forecasts from a conventional yet uncertain land surface model. Kalman filter estimates are only conditioned on observations prior to estimation times. Here it is argued that soil moisture estimation is a reanalysis-type problem as observations beyond the estimation time are useful in the estimation. An ensemble smoother is used in which the state vector and measurement vector are distributed in time and updated as a batch. Its performance in a land data assimilation context is compared to that of the ensemble Kalman filter. Results demonstrate that smoothing yields an improved estimate compared to filtering, reflected in the decreased deviation from truth and the reduction in uncertainty associated with the estimate. Precipitation significantly impacts the performance of the smoother, acting as an information barrier between dry-down events. An adaptive hybrid filter/smoother is presented in which brightness temperature is used to break the study interval into a series of dry-down events. The smoother is used on dry-down events, and the filter is used when precipitation is evident between estimation times. An improved estimate is obtained as all observations in a given dry-down period are used to estimate soil moisture in that period, and backward propagation of information from subsequent precipitation events is avoided. [ABSTRACT FROM AUTHOR]